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All Journal Jurnal Sains dan Teknologi Sistemasi: Jurnal Sistem Informasi Jurnal CoreIT Informatics for Educators and Professional : Journal of Informatics Jurnal Pengabdian Magister Pendidikan IPA Building of Informatics, Technology and Science Jurnal Manajemen Informatika dan Sistem Informasi JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Teknik Informatika (JUTIF) Media Jurnal Informatika ABDI MASSA: Jurnal Pengabdian Nasional Rengganis Jurnal Pengabdian Masyarakat Indo-MathEdu Intellectuals Journal Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) Journal of Artificial Intelligence and Digital Business Bakti Sekawan : Jurnal Pengabdian Masyarakat Jurnal Ilmiah Pengabdian dan Inovasi Indonesian Journal of Multidisciplinary on Social and Technology Kreativitas Pada Pengabdian Masyarakat (Krepa) SUBSERVE: Community Service and Empowerment Journal JUAN: Jurnal Pengabdian Nusantara ECONOMIST: Jurnal Ekonomi dan Bisnis Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Pada Masyarakat IPTEKS JAIM: Jurnal Aliansi Ilmu Multidisiplin
I Nyoman Switrayana
Universitas Bumigora
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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Pengaplikasian Convolutional Neural Network (MobileNetV3) Memanfaatkan Transfer Learning Untuk Membedakan Tanaman Cabai Berasal Dari Genus Capsicum Annuum Sujaka, Tomi Tri; Switrayana, I Nyoman; Haepa Fillah, Ibnu Mumtaz
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8740

Abstract

Accurate classification of Capsicum annuum varieties is crucial for food industry applications and agricultural research. Traditional manual classification methods are time-consuming, subjective, lack detail, and are prone to human error, requiring computer vision to automate them. This study presents learning in the form of automatic classification of nine diverse Capsicum annuum varieties using transfer learning with the MobileNetV3 architecture, which is designed to achieve high accuracy and be computationally energy efficient. The dataset consists of 4,500 images (training, testing, and validation) of 9 chili varieties: bell pepper, curly chili, cherry pepper, chiltepin, Hungarian wax, jalapeno, marconi, pequin, and Thai chili. This dataset goes through quality control, one of which is dataset balancing. The model in this study has also been optimized with Adam (Adaptive Moment Estimation). Model interpretation is also improved through Grad-CAM visualization, and model robustness has also been validated using cross-validation 5 times. This model achieved performance with a training accuracy of 97.2%, a testing accuracy of 95.1%, and a validation test of 94.8%, where 5-fold cross-validation showed consistent results (94.23% ± 1.45%). Grad-CAM analysis showed that this model focuses on structural features such as shape, surface texture, and color patterns. With the successful development of an AI system that can automatically identify chili varieties with an accuracy of 95.1%. This system works well in real conditions (90.6% accuracy) and is practical for use in agriculture and food processing. This technology can help farmers and food companies or lay people to sort chilies automatically, reduce costs, and improve quality control.
PELATIHAN TEKNOLOGI CERDAS UNTUK MEMULAI BISNIS START UP DAN MANAJEMEN KEUANGAN BISNIS PT. MELUKIS SENYUM INDAHMU I Nyoman Switrayana; L. Jatmiko Jati; Wisnu Alfiansyah; Muhlisin Muhlisin; Mohammad Ziad Anwar; Rini Adriani Auliana
Jurnal Pengabdian kepada Masyarakat Vol. 11 No. 2 (2024): JURNAL PENGABDIAN KEPADA MASYARAKAT 2024
Publisher : P3M Politeknik Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/abdimas.v11i2.6434

Abstract

The younger generation is creative and ambitious, often channeling this energy into startups. Observations by the community service team at Bumigora University reveal that most students aspire to establish startups to fulfill their needs and gain entrepreneurial experience. However, they face challenges such as limited knowledge of effective business strategies and financial management. This community service activity aims to enhance students' understanding of building and managing startups using smart technologies, particularly Artificial Intelligence (AI). The seminar introduces AI as a tool for developing business strategies, optimizing operations, and automating financial management. The program employs the Asset-Based Community Development (ABCD) method, focusing on leveraging community assets to address their needs. Activities include lectures, interactive sessions, and practical demonstrations to integrate AI into entrepreneurship. Results show a significant improvement in students' knowledge and skills in applying AI for business purposes. The seminar effectively enhanced their understanding of strategies for building businesses, designing marketing plans, and managing finances. Post-test results from participants via Google Form confirm these outcomes. This activity empowers the younger generation to establish competitive, technology-driven businesses.
OPTIMALISASI PENGERINGAN KOPI DAN KAKAO MELALUI INTEGRASI TEKNOLOGI ROCKET STOVE - SOLAR DOME BERBASIS BIOMASSA Husnita Komalasari; Destiana Adinda Putri; Indah Nalurita; Muhammad Nizhar Naufali; I Nyoman Switrayana; Ine Karni
SUBSERVE: Community Service and Empowerment Journal Vol. 4 No. 2 (2026): Juli 2026
Publisher : Prime Identity Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67766/scsej.v4i2.178

Abstract

Desa Selelos, Kecamatan Gangga, Kabupaten Lombok Utara memiliki potensi perkebunan kopi dan kakao yang cukup besar, namun proses pengeringan hasil panen masih dilakukan secara tradisional dan sangat bergantung pada kondisi cuaca. Kegiatan pengabdian ini bertujuan meningkatkan pengetahuan masyarakat serta meningkatkan efisiensi pengeringan kopi dan kakao melalui penerapan teknologi Rocket Stove–Solar Dome berbasis biomassa. Mitra kegiatan adalah Kelompok Tani Tumpang Sari. Metode kegiatan dilakukan melalui observasi, sosialisasi, pelatihan, penerapan teknologi, serta evaluasi menggunakan pre-test dan post-test. Teknologi yang diterapkan memanfaatkan kombinasi panas matahari dan sumber panas tambahan dari rocket stove berbahan bakar biomassa seperti tempurung kelapa dan bambu. Hasil kegiatan menunjukkan adanya peningkatan pengetahuan dan keterampilan peserta dengan rata-rata peningkatan kompetensi sebesar 50%. Selain itu, penggunaan teknologi Rocket Stove–Solar Dome mampu mempercepat waktu pengeringan dari 20–30 hari pada musim hujan menjadi 4–5 hari dengan kadar air akhir sebesar 8%. Teknologi ini memiliki keunggulan dalam menjaga kestabilan suhu pengeringan, mengurangi ketergantungan terhadap cuaca, serta memanfaatkan biomassa lokal yang ramah lingkungan. Dengan demikian, penerapan teknologi Rocket Stove–Solar Dome berpotensi meningkatkan efisiensi pengolahan pascapanen dan produktivitas kelompok tani secara berkelanjutan.
Local Wisdom-Based Treatment Recommendation System for Tropical Diseases Using Bayesian Network and Association Rule Mining Muhammad Haris Nasri; Rifqi Hammad; Pahrul Irfan; I Nyoman Switrayana; Rahayun Amrullah Husaini
Media Jurnal Informatika Vol 18 No 1 (2026): Media Jurnal Informatika
Publisher : Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v18i1.6290

Abstract

Tropical diseases remain a major public health problem in Indonesia, particularly in regions with limited access to healthcare facilities, leading communities to rely on traditional medicine based on local wisdom. However, the integration of traditional and modern treatment knowledge in intelligent recommendation systems remains limited. This study aimed to develop a tropical disease treatment recommendation system by integrating Bayesian Network (BN) and Association Rule Mining (ARM). Traditional and modern treatment knowledge were collected from scientific literature and expert interviews, validated by medical practitioners and traditional medicine experts, and incorporated into the system. A quantitative and experimental approach was conducted using a dataset of 150 tropical disease cases comprising dengue fever (42 cases), malaria (35), leptospirosis (28), tuberculosis (30), and leprosy (15). The dataset included 47 symptom attributes, 34 traditional treatment attributes, and 12 modern treatment attributes. Bayesian Network was used to model probabilistic relationships among symptoms, diagnoses, and treatments, while the Apriori algorithm in ARM was applied with minimum support and confidence thresholds of 0.3 and 0.7, respectively. Experimental evaluation on 30 testing cases showed that the integrated BN-ARM model achieved 86.7% accuracy and an F1-score of 86.0%, outperforming standalone BN (82.0% accuracy; F1-score 82.5%) and ARM (79.0% accuracy; F1-score 78.8%). The system generated accurate and contextually relevant treatment recommendations by combining local wisdom and modern medical knowledge.
Peningkatan Kompetensi Digital Siswa melalui Pelatihan Pengembangan Aplikasi Web dalam Mendukung Kualitas Sumber Daya Manusia Mohammad Najib Roodhi; Rahayun Amrullah Husaini; Gede Yogi Pratama; Rifqi Hammad; I Nyoman Switrayana; Muhammad Haris Nasri; Gilang Primajati
Rengganis Jurnal Pengabdian Masyarakat Vol. 6 No. 1 (2026): Mei 2026
Publisher : Pendidikan Matematika, FKIP Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/rengganis.v6i1.1102

Abstract

The rapid development of digital technology demands an increase in the quality of human resources (HR) that are adaptive to change, especially in the field of information technology. One of the competencies needed is the ability to develop web-based applications, which are increasingly relevant to industry needs. This community service activity aims to improve students' digital competencies and reduce the dynamic skills gap through web application development training. The partners in this activity were grade X students of SMKN 2 Mataram with a total of more than 20 participants. The training method used was a learning-by-doing approach, which included material delivery, demonstrations, direct practice, and evaluation. The results of the activity showed an increase in the average score of participants from 65 in the pre-test to 85 in the post-test, indicating a significant increase in participant understanding. In addition, participants were also able to develop simple web applications and demonstrated improved problem-solving skills and self-confidence. This activity contributes to improving digital competencies and strengthening the quality of human resources who are better prepared to face technological developments in the digital era.
A Multimodal Deep Learning Framework for Amyotrophic Lateral Sclerosis Diagnosis using Clinical and Audio Morphology Features I Nyoman Switrayana; Tomi Tri Sujaka; Imelda Silpiana Putri
SISTEMASI Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5763

Abstract

Amyotrophic Lateral Sclerosis (ALS) is a highly progressive neurodegenerative disease that impairs motor and speech function. Conventional diagnostic methods, both invasive and non-invasive, are often time-consuming and produce limited sensitivity. This leads to delays in treatment and worsening disease progression. This study proposes a multimodal deep learning framework that utilizes and integrates invasive medical records with non-invasive morphological features of patient speech audio extracted into Mel-Spectrograms. Unlike previous studies that focused solely on speech or clinical features, this study introduces an integrated multimodal diagnostic framework that effectively combines both data sources to achieve reliable diagnostic accuracy. The study included two experimental scenarios. In the first scenario, the audio-trained model used a Convolutional Neural Network (CNN) and was systematically optimized by testing variations in network depth, feature fusion techniques, and layer dropout probabilities to improve model generalization and stability. From the experimental results of the first scenario, the CNN achieved the best performance, achieving 80.33% accuracy in classification using audio data alone from all the tested model variations. In the second experimental scenario, when the best model was trained by incorporating clinical data, the model demonstrated improved diagnostic performance, achieving 100% accuracy. This finding highlights the importance of combining data modalities or sources from various domains, both invasive and non-invasive, to achieve optimal model performance for early ALS detection.
Perbandingan Kinerja Random Forest Regression dan Support Vector Regression dalam Forecasting Harga Saham Indeks LQ45 Githa Alfiansyah; Neny Sulistianingsih; I Nyoman Switrayana
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 5 No. 2 (2026): Mei-Juli
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v5i2.10759

Abstract

Forecasting harga saham merupakan tantangan yang kompleks karena sifat pergerakan data yang fluktuatif dan tidak linear, terutama pada saham indeks LQ45. Penelitian ini bertujuan untuk membandingkan kinerja algoritma Random Forest Regression dan Support Vector Regression dalam meramalkan harga penutupan saham BBRI, TLKM, dan ANTM. Pemodelan dilakukan menggunakan data historis periode 1 Januari 2020 hingga 30 april 2026, dengan memanfaatkan ekstraksi fitur lag 1 sampai 5 dan Moving Average MA5, MA10. Evaluasi kinerja model dilakukan dalam dua skenario, yaitu parameter default dan tuning parameter secara manual, serta diukur menggunakan metrik Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), dan Mean Absolute Percentage Error (MAPE). Hasil penelitian menunjukkan bahwa Support Vector Regression memiliki kinerja dan ketangguhan (robust) yang lebih unggul dibandingkan Random Forest Regression. Proses optimasi menunjukkan bahwa kernel linear terbukti menjadi yang terbaik untuk Support Vector Regression dalam peramalan harga saham karena konsisten menghasilkan tingkat kesalahan minimum. Support Vector Regression dengan kernel linear mencapai nilai evaluasi MAE 50,25; RMSE 66,07; dan MAPE 1,36% pada saham BBRI, serta MAE 65,44; RMSE 89,25; dan MAPE 1,92% pada saham TLKM. Keunggulan Support Vector Regression terlihat sangat signifikan pada saham ANTM yang memiliki volatilitas tinggi, di mana Support Vector Regression sukses menekan kesalahan hingga MAPE 2,65%, sedangkan Random Forest Regression gagal mengekstrapolasi tren lonjakan harga. Dengan demikian, Support Vector Regression berkonfigurasi kernel linear dinobatkan sebagai model peramalan yang paling direkomendasikan karena akurat dan stabil dalam merespons fluktuasi pasar modal.
Klasifikasi Kematangan Stroberi Menggunakan OpenCV, CNN EfficientNet-B0, dan Grad-CAM Berbasis Web Dion Arifin; Tomi Tri Sujaka; I Nyoman Switrayana
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 5 No. 2 (2026): Mei-Juli
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v5i2.12873

Abstract

Penilaian tingkat kematangan buah stroberi secara manual bersifat subjektif, memerlukan pengalaman, dan memakan waktu, sehingga dibutuhkan sistem klasifikasi otomatis yang akurat, transparan, serta mudah diakses. Penelitian ini mengembangkan sistem klasifikasi tingkat kematangan stroberi berbasis web dengan memanfaatkan Open Source Computer Vision Library (OpenCV) untuk pemrosesan citra dan Convolutional Neural Network berarsitektur EfficientNet-B0 melalui transfer learning dengan strategi partial fine-tuning. Dataset terdiri atas 677 citra stroberi yang diperoleh dari Kecamatan Sembalun, Kabupaten Lombok Timur, dan repositori publik, kemudian dikelompokkan ke dalam kelas “Dapat Dipetik” dan “Tidak Dapat Dipetik”. Data dibagi menggunakan stratified random sampling melalui tiga skenario rasio latih, validasi, dan uji, yaitu 80:10:10, 70:15:15, dan 60:20:20. Transparansi keputusan model diwujudkan melalui Gradient-weighted Class Activation Mapping (Grad-CAM) yang diproses bersama OpenCV untuk menghasilkan heatmap dan overlay. Sistem dikembangkan menggunakan FastAPI, dikemas melalui Docker, dan disebarkan pada Hugging Face Spaces. Hasil pengujian menunjukkan bahwa rasio 80:10:10 menghasilkan kinerja paling stabil dengan akurasi validasi akhir sebesar 97,79% dan F1-score rata-rata tertimbang sebesar 97,79%. Visualisasi Grad-CAM menunjukkan bahwa model berfokus pada tekstur dan pigmentasi buah, bukan pada latar belakang. Temuan ini menunjukkan bahwa integrasi EfficientNet-B0, Grad-CAM, OpenCV, dan layanan web mampu menyediakan klasifikasi kematangan stroberi yang akurat, dapat dijelaskan, responsif, dan berpotensi mendukung pengambilan keputusan panen.
Klasifikasi Uang Kertas dan Logam Asia Tenggara Menggunakan CNN MobileNetV3 Berbasis Web Bagas Edra Athallah Rafif; Bambang Krismono Triwijoyo; I Nyoman Switrayana
Indonesian Journal of Multidisciplinary on Social and Technology Vol. 4 No. 3 (2026): Juli - Oktober
Publisher : PT Ilmu Data Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/ijmst.v4i3.13030

Abstract

Perbedaan karakteristik visual uang dari berbagai negara di Asia Tenggara menyebabkan proses identifikasi uang menjadi cukup sulit dilakukan secara manual, khususnya bagi masyarakat umum maupun wisatawan. Penelitian ini bertujuan mengembangkan sistem klasifikasi uang kertas dan logam Asia Tenggara berbasis web menggunakan Convolutional Neural Network (CNN) dengan arsitektur MobileNetV3 melalui pendekatan transfer learning. Dataset yang digunakan terdiri atas 1.610 citra yang terbagi ke dalam 21 kelas, meliputi uang kertas, uang logam dari sepuluh negara Asia Tenggara, serta satu kelas non-uang. Tahap penelitian meliputi preprocessing, augmentasi data, pelatihan model menggunakan Stratified K-Fold Cross Validation dengan variasi K=5, K=10, dan K=15, serta implementasi model ke dalam aplikasi web. Hasil penelitian menunjukkan bahwa konfigurasi K=10 memberikan performa terbaik dengan rata-rata accuracy sebesar 93,91%, precision sebesar 94,72%, recall sebesar 93,97%, dan F1-score sebesar 93,97%. Hasil tersebut menunjukkan bahwa MobileNetV3 mampu memberikan performa klasifikasi yang baik dan stabil sehingga layak diterapkan sebagai sistem identifikasi uang Asia Tenggara berbasis web.
Integrasi Association Rule Mining dan Cost-Plus Pricing untuk Optimasi Paket Produk dan Profitabilitas UMKM Muhammad Haris Nasri; Gede Yogi Pratama; Rifqi Hammad; I Nyoman Switrayana; Rahayun Amrullah Husaini
INFORMATICS FOR EDUCATORS AND PROFESSIONAL : Journal of Informatics Vol. 11 No. 1 (2026): INFORMATICS FOR EDUCATORS AND PROFESSIONAL : JOURNAL OF INFORMATICS (Juni 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Bina Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51211/itbi.v11i1.3886

Abstract

Micro, Small, and Medium Enterprises (MSMEs) play a vital role in the economy, yet they still face challenges in developing product packages and optimal pricing. This study aims to integrate the Apriori algorithm and the Cost-Plus Pricing method in developing product packages and determining optimal selling prices. The data used are 2,000 MSME sales transactions processed through preprocessing, data transformation, and analysis using the Apriori algorithm with a minimum support of 0.01 and a confidence of 0.4. The results show that 54 frequent itemsets and association rules were obtained with an average support value of 0.564, a confidence of 0.926, and a lift of 1.694. The best rule produces two main product packages, namely the Coffee and Dodol package and the Chicken Special Grill, Plecing Kangkung, and Sambal package. Furthermore, the package prices were determined using the Cost-Plus Pricing method and a profit increase simulation was conducted. The results show that profits increased from 48,293,400 to 53,517,360, representing an increase of 5,223,960. Thus, the integration of these two methods has proven effective in increasing profitability and can be used as a data-driven marketing strategy for MSMEs.
Co-Authors Abdurahman Abdurrahman Adinda Mutia Gani Afrig Aminuddin Apriani Apriani Arie Cahyani Ashadi, Diki Bagas Edra Athallah Rafif Baiq Rabiatul Adawiyah Kartika Wulan Bukran Bukran Darmawan, I Made Angga Wahyu Destiana Adinda Putri Diki Ashadi Dion Arifin Fitri, Yelli Galuh Tinezia Endriyanto Gede Yogi Pratama Gilang Primajati Githa Alfiansyah Gozin Najah Rusyada Haepa Fillah, Ibnu Mumtaz Hairani Hairani Hammad, Rifqi Husnita Komalasari I Wayan Bratayasa I Wayan Mustika Nayottama Adi Wijaya Ibrahim, Isra Dewi Kuntary Imelda Silpiana Putri Indah Nalurita Ine Karni Kartika Gemma Pravitri Ketut Widya Kayohana Kurniadin Abd Latif Logi Mulawarman Logi Mulawarman Mohammad Najib Roodhi Mohammad Ziad Anwar Muhamad Azwar, Muhamad Muhamad Wisnu Alfiansyah Muhammad Haris Nasri Muhammad Mujahid Dakwah Muhammad Nizhar Naufali Muhammad Tahir Muhammad Tahir Muhlisin Muhlisin Mulawarman, Logi Nabila, Fitri Neny Sulistianingsih Pahrul Irfan Pahrul Irfan Pratama, Gede Yogi Rahayun Amrullah Husaini Ridha Nurul Hayati Rifqi Hammad Rini Adriani Auliana Rini Anggriani Risyaf Kudus Pranasa Rizal Ramdani Rusyada, Gozin Najah Sirojul Hadi Siti Fatimah Solina, Eva Sujaka, Tomi Tri Susilo Talidobel Syafira Mahfuzi Ardiyati Syahrir, Moch. Tomi Tri Sujaka Tomi Tri Sujaka Triwijoyo, Bambang Krismono Wisnu Alfiansyah Zamroni Alpian Muhtarom Zefanya Andryan Girsang